CN110244753B - Wind speed measuring and calculating method and unmanned aerial vehicle - Google Patents

Wind speed measuring and calculating method and unmanned aerial vehicle Download PDF

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CN110244753B
CN110244753B CN201910549268.3A CN201910549268A CN110244753B CN 110244753 B CN110244753 B CN 110244753B CN 201910549268 A CN201910549268 A CN 201910549268A CN 110244753 B CN110244753 B CN 110244753B
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aerial vehicle
unmanned aerial
wind
speed
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CN110244753A (en
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张添保
陈刚
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Shenzhen Autel Intelligent Aviation Technology Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C39/00—Aircraft not otherwise provided for
    • B64C39/02—Aircraft not otherwise provided for characterised by special use
    • B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U50/00—Propulsion; Power supply
    • B64U50/10—Propulsion
    • B64U50/19—Propulsion using electrically powered motors
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0011—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement
    • G05D1/0016—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement characterised by the operator's input device
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B64U2201/20—Remote controls

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Abstract

The embodiment of the invention relates to a wind speed measuring and calculating method and an unmanned aerial vehicle. The wind speed measuring and calculating method comprises the following steps: acquiring flight data information of the unmanned aerial vehicle, wherein the flight data information comprises a current attitude angle, a current speed and a current acceleration of the unmanned aerial vehicle; establishing a speed observation model of the unmanned aerial vehicle by using the flight data information so as to obtain a speed observation value of the unmanned aerial vehicle; acquiring an observed value of wind power borne by the unmanned aerial vehicle according to the speed observed value; and calculating the wind speed in the current flight environment according to the observed value of the wind power. The method realizes wind speed measurement and calculation without depending on a newly added wind speed sensor and an external database, saves the cost of hardware equipment, does not bring additional calculation burden and the problem of instantaneity, and is simple and low in cost. Moreover, based on the calculation result of wind power measurement and calculation, the method can be applied to an early warning function, prompts a user, and reduces the probability of unmanned aerial vehicle accidents.

Description

Wind speed measuring and calculating method and unmanned aerial vehicle
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of unmanned aerial vehicles, in particular to a wind speed measuring and calculating method and an unmanned aerial vehicle.
[ background ] A method for producing a semiconductor device
The unmanned aerial vehicle has strong adaptability, low use cost and wide application in different occasions by throwing the hovering aerial vehicle quickly and conveniently. It can play an important role by carrying different types of functional components.
During flight, no one can be disturbed by wind. When wind speed or wind power are small, the robustness of the flight control system can resist wind interference, and stable flight of the unmanned aerial vehicle is guaranteed.
However, the range of wind forces that the flight control system can accommodate or resist is within certain limits. After the wind speed surpassed the upper limit that unmanned aerial vehicle can bear, flight control system's stability will be difficult to maintain, the unable accident such as exploding of unmanned aerial vehicle, explosive device even appears easily. Especially, when the unmanned aerial vehicle that takes photo by plane when wind speed is great, its quality of taking photo by plane can receive the influence seriously.
Therefore, the wind speed or wind detection function is a very important function for the drone. Can provide better alarming function for the unmanned aerial vehicle user based on unmanned aerial vehicle wind speed detects and estimates, avoid the emergence of accident.
The current wind speed detection or estimation methods can be roughly divided into two methods, namely directly measuring the air speed by using a wind speed sensor and estimating the wind speed by using a method of establishing a database in advance or a big data-based method. However, the method of directly measuring the airflow speed by using the wind speed or the wind sensor needs to add an additional sensor to the unmanned aerial vehicle, which increases the manufacturing cost of the unmanned aerial vehicle. The method for establishing the database or calculating the big data consumes more calculation power, and the calculation burden of the flight control system is increased. Moreover, the database is loaded on the airplane, so that the memory is greatly occupied, the consumed time is more, and the real-time performance of the wind speed detection is greatly influenced.
[ summary of the invention ]
In order to solve the technical problem, the embodiment of the invention provides a wind speed measuring and calculating method independent of a database and a newly-added wind speed sensor and an unmanned aerial vehicle.
In order to solve the above technical problems, embodiments of the present invention provide the following technical solutions: a wind speed measuring and calculating method. The wind speed measuring and calculating method comprises the following steps:
acquiring flight data information of the unmanned aerial vehicle, wherein the flight data information comprises a current attitude angle, a current speed and a current acceleration of the unmanned aerial vehicle;
establishing a speed observation model of the unmanned aerial vehicle by using the flight data information to obtain a speed observation value of the unmanned aerial vehicle;
acquiring an observed value of wind power borne by the unmanned aerial vehicle according to the speed observed value;
and calculating the wind speed in the current flight environment according to the observed value of the wind power.
Optionally, said using said flight data information to build a velocity observation model of said drone to obtain a velocity observation of said drone, comprising:
establishing a speed equation of the unmanned aerial vehicle;
and establishing the speed observation model of the unmanned aerial vehicle according to the speed equation of the unmanned aerial vehicle so as to obtain the speed observation value of the unmanned aerial vehicle.
Optionally, the velocity equation of the drone is:
Figure BDA0002105020900000021
Figure BDA0002105020900000022
wherein, V x And V y The speeds of the unmanned aerial vehicle in the x direction and the y direction in the flight plane of the unmanned aerial vehicle are respectively, T is the tension of a propeller, theta is the pitch angle of the unmanned aerial vehicle,
Figure BDA0002105020900000023
rho is the air density at the flight altitude of the unmanned aerial vehicle, S is the roll angle of the unmanned aerial vehicle fb Is the frontal area, S, of the flight of the unmanned aerial vehicle along the x direction rl For the frontal area, F, of the unmanned aerial vehicle when flying in the y-direction wx And F wy For the wind forces respectively experienced by the drone in the x-direction and the y-direction,C dx and C dy Respectively, be the coefficient of resistance that unmanned aerial vehicle flies along the x direction and flies along the y direction, m is unmanned aerial vehicle's quality.
Optionally, the speed observation model of the drone is:
Figure BDA0002105020900000031
wherein,
Figure BDA0002105020900000032
a velocity change rate observation updated for the drone in an x direction,
Figure BDA0002105020900000033
updating a speed rate observation for the drone in a y direction;
Figure BDA0002105020900000034
the observed value of the speed of the unmanned aerial vehicle in the x direction is obtained;
Figure BDA0002105020900000035
the observed value of the speed of the unmanned aerial vehicle in the y direction is obtained; v x The actual speed value of the unmanned aerial vehicle in the x direction is obtained; v y The actual speed value of the unmanned aerial vehicle in the y direction is obtained; t is the propeller tension; theta is the pitch angle of the unmanned aerial vehicle; phi is the roll angle of the unmanned aerial vehicle; rho is the air density of the altitude where the unmanned aerial vehicle is located; s. the fb Is the windward area S of the unmanned aerial vehicle flying along the x direction rl Frontal area for unmanned aerial vehicle flying in y direction, C dx Coefficient of drag for unmanned aerial vehicle flying in the x direction, C dy Is the drag coefficient of the unmanned plane flying along the y direction,
Figure BDA0002105020900000036
for the observed value of the wind power of the unmanned plane in the x direction,
Figure BDA0002105020900000037
the wind power observed value of the unmanned aerial vehicle in the y direction is obtained; m is the mass of the unmanned aerial vehicle; l is 1x And L 1y Is a speed adjustment coefficient for correcting errors.
Optionally, the frontal area is determined by calculating as follows:
S fb ＝S fb0 (1+f fb (θ,φ))
S rl ＝S rl0 (1+f rl (θ,φ))
wherein S is fb Is the windward area S of the unmanned plane flying along the x direction rl The frontal area of the unmanned aerial vehicle flying along the x direction; s fb0 When the attitude angle is 0, the windward area of the unmanned aerial vehicle flying along the x direction; s. the rl0 When the attitude angle is 0, the windward area of the unmanned aerial vehicle flying along the y direction; f. of fb (theta, phi) and f rl (θ, φ) is a nonlinear function; theta is a pitch angle; phi is the rolling angle.
Optionally, the propeller tension is calculated by the following formula:
Figure BDA0002105020900000038
wherein, a z The acceleration of the unmanned aerial vehicle in the z direction is shown, and g is the gravity acceleration; the z direction is perpendicular to a plane formed by the x direction and the y direction; theta is a pitch angle; phi is a rolling angle; and m is the mass of the unmanned aerial vehicle.
Optionally, the resistance coefficient is obtained by calculating as follows:
Figure BDA0002105020900000041
wherein, C dx Coefficient of drag for unmanned aerial vehicle flying in the x direction, C dy Is the drag coefficient of the unmanned aerial vehicle flying along the y direction, a x Under the windless condition, the acceleration of the unmanned aerial vehicle in the x direction; a is a y Under the windless condition, the acceleration of the unmanned aerial vehicle in the y direction;
t is the propeller tension; theta is a pitch angle; phi is a rolling angle; rho is the air density of the altitude where the unmanned aerial vehicle is located; s. the fb Is the windward area S of the unmanned plane flying along the x direction rl For the frontal area of unmanned aerial vehicle when flying along the y direction, m is unmanned aerial vehicle's quality.
Optionally, the obtaining, according to the speed observation value, an observation value of wind force borne by the unmanned aerial vehicle includes:
calculating an observed value of wind power borne by the unmanned aerial vehicle according to the following formula:
Figure BDA0002105020900000042
wherein,
Figure BDA0002105020900000043
for the observed value of the wind power change rate of the unmanned aerial vehicle in the y direction,
Figure BDA0002105020900000044
is the observed value of the wind power change rate of the unmanned plane in the x direction,
Figure BDA0002105020900000045
is composed of
Figure BDA0002105020900000046
Represents the wind observation in the x-direction,
Figure BDA0002105020900000047
is composed of
Figure BDA0002105020900000048
Represents the wind observation in the y-direction, m is the mass of the drone, L 2x And L 2y Adjusting the coefficient for the wind power;
Figure BDA0002105020900000049
the speed observed value of the unmanned aerial vehicle in the x direction is obtained;
Figure BDA00021050209000000410
and the speed observed value of the unmanned aerial vehicle in the y direction is obtained.
Optionally, the calculating the wind speed in the current environment according to the observed value of the wind power includes:
calculating the wind speed of the unmanned aerial vehicle in the current environment by using the following formula:
Figure BDA00021050209000000411
Figure BDA00021050209000000412
wherein, F wx For the observation of the wind force of the unmanned aerial vehicle in the x direction, F wy The wind power observed value of the unmanned aerial vehicle in the y direction is obtained; rho is the air density of the altitude where the unmanned aerial vehicle is located; s. the fb Is the windward area S of the unmanned aerial vehicle flying along the x direction rl Frontal area for unmanned aerial vehicle flying in y direction, C dx Coefficient of drag, C, for unmanned aerial vehicle flying in the x-direction dy The drag coefficient of the unmanned aerial vehicle flying along the y direction.
Optionally, the method further comprises: and acquiring the wind direction under the current environment according to the wind speed under the current environment.
Optionally, the wind direction in the current environment satisfies the following expression:
β＝ψ+arctan2(-V wx ,-V wy )
where psi is the yaw angle of the drone, beta is the wind direction, V wx Is the wind speed in the x direction, V wy Is the wind speed in the y direction.
Optionally, the method further comprises: and when the wind speed under the current environment exceeds a preset value, sending an alarm signal to a user.
Optionally, the method further comprises: and sending the wind speed and the wind direction under the current environment to a user terminal and displaying the wind speed and the wind direction on the user terminal.
Another embodiment of the invention provides an unmanned aerial vehicle. The unmanned aerial vehicle comprises a body; the machine arm is connected with the machine body; the power device is arranged on the horn and used for providing flying power for the unmanned aerial vehicle; the flight controller is arranged on the airplane body;
wherein the flight controller includes: at least one processor; and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the wind anemometry method as described above.
Compared with the prior art, the wind power measuring and calculating method provided by the embodiment of the invention realizes wind power or wind speed measuring and calculating by utilizing the principle of the disturbance observer on the premise of not depending on the newly added wind speed sensor and an external database, so that the cost of hardware equipment is saved, the problems of extra calculation burden and instantaneity are not brought, and the method is simple and low in cost.
Based on the calculation result of wind measurement and calculation, the method can be further applied to an early warning function, and can prompt or give an alarm to a user, so that the probability of accidents of the unmanned aerial vehicle is reduced.
[ description of the drawings ]
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
FIG. 1 is a schematic diagram of an application environment of an embodiment of the present invention;
fig. 2 is a functional block diagram of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a display interface of a remote controller according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a display interface of an intelligent terminal according to an embodiment of the present invention;
FIG. 5 is a functional block diagram of a wind observer provided in an embodiment of the invention;
FIG. 6 is a method flowchart of a wind estimation method according to an embodiment of the present invention;
fig. 7 is a flowchart of a method of an unmanned aerial vehicle early warning method according to an embodiment of the present invention;
FIG. 8 is a graph of wind speed versus time provided by an embodiment of the present invention;
fig. 9 is a graph of wind direction as a function of time, provided by an embodiment of the invention.
[ detailed description ] embodiments
In order to facilitate an understanding of the invention, the invention is described in more detail below with reference to the accompanying drawings and specific examples. It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present. As used in this specification, the terms "upper," "lower," "inner," "outer," "bottom," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the invention and simplicity in description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Furthermore, the technical features mentioned in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.
The 'disturbance observer' refers to a disturbance detection strategy which is equivalent to observe disturbance due to external disturbance and the difference between the actual object state caused by the change of model parameters and the nominal model calculation result. The external disturbance may be a disturbance applied to a certain motion system, for example, the influence on the motion of the drone caused by wind disturbance during the flight of the drone.
Fig. 1 is an application environment provided by an embodiment of the present invention. As shown in fig. 1, the application environment is an example of a drone system, and includes a drone 10, a remote control device 20, and a wireless network 30.
The drone 10 may be any type of powered (e.g., electric) unmanned aerial vehicle including, but not limited to, quad drones, fixed wing aircraft, helicopter models, and the like. In this embodiment, a four-axis drone is taken as an example for presentation. The main body of the unmanned aerial vehicle 10 may be loaded with a plurality of different functional modules, and these functional modules may be software modules, hardware modules, or a combination of software and hardware, and are a modular device for implementing one or more functions.
In some embodiments, the drone 10 may include a fuselage, a horn, a power plant, and a flight controller. Wherein the fuselage is the main structure of the drone 10 for providing sufficient space to accommodate one or more components. It can have a proper volume and shape according to actual conditions and is made of corresponding materials.
The horn is a part extending outward from the body, and serves as a mounting or fixing structure for an unmanned aerial vehicle power device such as a propeller. The horn can adopt integrated into one piece's structure with the fuselage, also can be connected with the fuselage in the form of can dismantling the connection. Typically, on four axis unmanned aerial vehicle, the horn can set up to 4, extends along diagonal symmetry, forms the mounted position of four screw propellers.
The power device can specifically be that adopt any energy type driven, and the installation is fixed at the terminal mounted position of horn for provide the constructional device of flight power for unmanned aerial vehicle. For example by means of a motor-driven propeller. The power which can be provided by the power device or the actually adopted structure can be determined according to the needs of actual conditions.
The flight controller is an unmanned aerial vehicle control core built in the fuselage. It may be any type of electronic device with appropriate logic and computational capabilities, including but not limited to processor chips based on large scale integrated circuit implementations, an integrated system on a chip (SOC), and a processor and storage medium connected by a bus.
The remote control device 20 may be of any type for establishing a communication connection with the drone, controlling the means of the drone, such as a remote control. The remote controller can be equipped with one or more different user interaction devices, and based on the user interaction devices, user instructions are collected or information is displayed and fed back to a user, so that interaction between the user and the unmanned aerial vehicle is realized.
These interaction means include, but are not limited to: keys, a scroll wheel, a display screen, a touch screen, a mouse, a speaker and a joystick. For example, the remote control device 20 may be equipped with a display screen through which a user's remote control commands to the drone are received and aerial images are presented to the user, or a corresponding simulated driving interface is presented to the user, on which one or more flight parameters, such as flight speed, heading, or remaining power, are presented.
In other embodiments, the remote control device 20 may also be implemented by a smart terminal. The intelligent terminal comprises but is not limited to a smart phone, a tablet computer, a portable computer, a wearable device and the like. This intelligent terminal establishes communication connection through the APP customer end or the webpage end of the specific setting of operation and unmanned aerial vehicle, realizes and unmanned aerial vehicle between the data receiving and dispatching.
The wireless network 30 may be a wireless communication network based on any type of data transmission principle for establishing a data transmission channel between two nodes. Such as a bluetooth network, a WiFi network, a wireless cellular network, or a combination thereof, located in different signal frequency bands. The frequency band or network form in which the wireless network 30 is specifically used is related to the communication devices employed by the drone 10 and the remote control device 20.
Fig. 2 is a functional block diagram of the unmanned aerial vehicle 10 according to the embodiment of the present invention. In some embodiments, as shown in fig. 2, the functional modules carried by the drone 10 may include: a sensor 11, a memory 12, and a processor 13.
The sensor 11 is a sensor, such as a six-axis gyroscope, an accelerometer, or the like, disposed in the main body of the unmanned aerial vehicle and used for detecting a motion state parameter of the unmanned aerial vehicle during a flight process. These sensors 11 are the basic sensors in the design and manufacture of the drone 10 to monitor the current state of motion of the drone 10 to achieve effective control of the flight of the drone 10.
The memory 12 is a non-volatile computer-readable storage medium, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. The system comprises a program storage area and a data storage area, which are respectively used for storing corresponding data information, such as nonvolatile software programs, nonvolatile computer executable programs and modules stored in the program storage area, or operation processing results, shot image information and the like stored in the data storage area.
The processor 13 is the core of the flight control of the unmanned aerial vehicle, and any type of processor can be specifically adopted as the core of the logic processing and operation. The processor 13 is in communication connection with the memory 12, and is configured to acquire data, execute a logical operation function, issue an operation processing result, change a flight state of the unmanned aerial vehicle 10 according to a user instruction, and ensure that the unmanned aerial vehicle 10 is in a safe and controllable flight state.
In one aspect, the processor 13 may obtain one or more collected data from the sensor 11, and analyze and determine several items of data information (such as attitude angle, acceleration, flight speed, and the like) related to the drone through a set data fusion or analysis method, as a basis for controlling the motion state of the drone. On the other hand, the flight control system 13 is also connected to the memory 12, and calls a corresponding software program or a computer-executable program in the memory 12 to execute a corresponding logical operation function, thereby performing a corresponding operation and determination.
To implement the wind speed warning function, please continue to refer to fig. 2, the processor 13 can also read the computer program instructions in the memory 12 to implement the two functional modules of the wind observer 131 and the monitor 132. Wherein the wind observer 131 is used for detecting wind related data. The monitor 132 is used to keep a check on wind related data and issue a warning signal in a timely manner.
In the flight process of the unmanned aerial vehicle, the wind observer 131 can read the relevant data information of the unmanned aerial vehicle, estimate the current wind interference on the unmanned aerial vehicle by using the principle of the interference observer, and output a corresponding wind observation value. The monitor 132 receives the wind observation value output by the wind observer 231, compares the wind observation value with a preset warning condition, and determines whether a warning signal needs to be triggered at the moment.
After triggering the warning signal, the drone 10 will feed back to the remote control device 20 through the wireless network 30. After receiving the warning signal, the remote control device 20 may display a corresponding warning prompt message through the interactive device, so as to remind the operator to pay attention to flight safety, land to a suitable place in time, and the like.
For example, when the remote control device 20 is a remote controller, the user may be prompted at the center of the simulated driving interface using a display interface such as that shown in fig. 3 — a "wind speed is high". When the remote control device 20 is an intelligent terminal, as shown in fig. 4, prompt messages (Tips) may be displayed in a local area of a display screen of the intelligent terminal. Alternatively, a special warning sound may be played through a speaker of the remote control device 20 to warn that the current wind speed is too high.
Based on the wind observation value provided by the wind observer, in other embodiments, the monitor may further convert the wind observation value into wind speed and wind direction data currently received by the unmanned aerial vehicle and provide the wind speed and wind direction data to the remote control device 20, and the wind speed and wind direction are displayed by an interactive device such as a display screen of the remote control device 20, so that an operator can timely know the wind condition of the current flight space.
In the application environment shown in fig. 1, only the application of the wind speed observation and early warning function to the unmanned aerial vehicle system is shown. It will be understood by those skilled in the art that the functional modules (such as a wind observer, a monitor, etc.) for implementing the wind speed observation and early warning function can also be mounted on other types of mobile vehicles (such as a remote control vehicle), and the same or similar early warning function can be implemented by receiving data information collected by one or more basic sensors in the mobile vehicle. The inventive concept disclosed in the embodiments of the present invention is not limited to the application to the unmanned aerial vehicle system shown in fig. 1.
Based on the inventive idea of observing wind speed by using a wind speed observer disclosed in the embodiment of the present invention, according to practical needs or usage scenarios of an unmanned aerial vehicle, etc., one skilled in the art can easily think of adjusting, replacing or changing one or more steps and parameters thereof to construct other alternative models. These surrogate models are all derived by reasonable derivation by those skilled in the art based on the present invention, considering from different sides of the drone.
For example, disturbance of wind power can be observed by detecting changes in attitude angle of the drone when hovering. The method of using a force balance principle, a system identification principle and the like can be replaced to determine the interference amount of wind power to the operation of the unmanned aerial vehicle, and then the estimation of the wind power is completed.
The following describes in detail the wind estimation process based on the interference observation principle. The process of the disturbance observer can be roughly divided into two parts, namely state observation and disturbance observation. Fig. 5 is a functional block diagram of a wind observer provided in an embodiment of the present invention. As shown in fig. 5, the wind observer comprises a state observation unit 1311 and a disturbance observation unit 1312.
Wherein, a speed calculation model is arranged in the state observation unit 1311. State observation section 1311 predicts a velocity observation value corresponding to the currently input data information using the velocity calculation model, and outputs the value. The velocity observation is a theoretical airspeed determined by simulation or calculation from a velocity calculation model.
The speed calculation model is an empirical function, and the theoretical flying speed of the unmanned aerial vehicle at the moment can be calculated and output according to input data information. The specific velocity calculation model may be determined by one skilled in the art based on underlying velocity dynamics equations, in conjunction with theoretical analysis and/or experimental data fitting.
In some embodiments, the velocity dynamics equation of the drone in flight state may be represented by equation (1):
Figure BDA0002105020900000101
in equation (1), the rate of change of the flight speed of the drone is decomposed into two orthogonal vectors
Figure BDA0002105020900000102
And
Figure BDA0002105020900000103
to indicate. The directions of the two orthogonal vectors are the x direction and the y direction respectively, and the two directions are perpendicular to each other.
Wherein,
Figure BDA0002105020900000104
for the rate of change of speed (i.e. acceleration) of the drone in the x direction,
Figure BDA0002105020900000105
integrating the speed change rate (namely the acceleration) of the unmanned aerial vehicle in the y direction to obtain a corresponding predicted speed value, wherein T is the propeller tension, theta is the pitch angle, phi is the roll angle, rho is the air density of the altitude where the unmanned aerial vehicle is located, and S fb Is the windward area S of the unmanned plane flying along the x direction rl Frontal area for unmanned aerial vehicle flying in y direction, C dx Coefficient of drag for unmanned aerial vehicle flying in the x direction, C dy Coefficient of drag for unmanned aerial vehicle flying in the y-direction, F wx For unmanned aerial vehicle wind in the x direction, F wy For unmanned aerial vehicle wind-force in the y direction, m is unmanned aerial vehicle's quality.
Further, the frontal area in equation (1) varies with the attitude angle of the unmanned aerial vehicle during flight, which can be approximately considered as a nonlinear function related to the attitude angle. For example, the nonlinear function can be written in the form shown in equations (2) and (3) below:
S fb ＝S fb0 (1+f fb (θ,φ)) (2)
S rl ＝S rl0 (1+f rl (θ,φ)) (3)
wherein S is fb0 When the attitude angle is 0, the windward area of the unmanned aerial vehicle flying along the x direction; s rl0 When the attitude angle is 0, the windward area of the unmanned aerial vehicle flying along the y direction.
The resistance coefficient is attribute data uniquely determined by factors such as the appearance and the structure of the unmanned aerial vehicle, and cannot be changed along with the change of the motion state of the unmanned aerial vehicle. This attribute data can be measured by flight experiments of the drone in indoor windless environments (excluding wind disturbances).
The propeller pulling force is related to the output power of the motor, and the propeller pulling force externally shows the acceleration of the unmanned aerial vehicle. Generally, greater acceleration also means higher output propeller drag. In some embodiments, the propeller tension may be calculated by equation (4) as follows:
Figure BDA0002105020900000111
wherein, a z The acceleration of the unmanned aerial vehicle in the z direction is shown, and g is the gravity acceleration; the z-direction is perpendicular to a plane formed by the x-direction and the y-direction.
In summary, as will be understood by those skilled in the art, when the state observation unit performs state observation based on the above speed dynamic equation to predict the flight speed of the aircraft, the data information required to be input includes: the attitude angle, the flight speed and the acceleration of the unmanned aerial vehicle, the mass of the unmanned aerial vehicle, the resistance coefficient of the unmanned aerial vehicle during flight and a nonlinear function for calculating the windward area.
Wherein, the attitude angle, the flight speed and the acceleration of unmanned aerial vehicle all are the flight data that change along with unmanned aerial vehicle's motion state. The mass of the unmanned aerial vehicle, the drag coefficient of the unmanned aerial vehicle during flying and the nonlinear function for calculating the windward area are inherent attribute data of the unmanned aerial vehicle, and can be calculated and determined or measured and determined in advance through experiments and the like.
Therefore, the flight DATA DATA1 of the dynamic changes of the attitude angle, the flight speed and the acceleration of the unmanned aerial vehicle can be obtained by the flight control system through calculation in a DATA fusion mode according to the sensor DATA acquired by the sensor.
And attribute DATA DATA2 related to the characteristics of the unmanned aerial vehicle, such as the mass of the unmanned aerial vehicle, the drag coefficient of the unmanned aerial vehicle in flight, a nonlinear function for calculating the windward area and the like, can be stored in a memory in advance for being called by the state observation unit.
The calculated flying speed of the speed calculation model is not consistent with the actual flying speed due to the simulation error of the model and other interference. In this embodiment, the factors that will cause the inconsistency are all equivalent to the wind disturbances experienced by the drone. Therefore, the interference observation unit 1312 is configured to calculate and determine a predicted wind value through a suitable calculation formula according to a difference between the speed observation value and the actual flight speed of the unmanned aerial vehicle, so as to complete interference observation.
In the equation (1) provided in the above embodiment, the variable of the wind force to which the unmanned aerial vehicle is subjected is also included. In this way, the wind interference currently suffered by the unmanned aerial vehicle needs to be input into the speed calculation model used by the state observation unit.
Thus, in some embodiments, continuing to refer to FIG. 3, the output of the disturbance observation unit 1312 is also used as one of the inputs DATA3 (i.e., wind observation) of the state observation unit 1311. The wind observed value obtained by calculation is fed back to the state observation unit 1311 for updating the speed observed value and obtaining a more accurate estimated value.
It will be appreciated that there are unknown observed variables (e.g., wind observations) at the initial run. Therefore, the state observation unit 1311 needs to be initialized, and an initial value is first assigned to the unknown variables (e.g., the wind observation value is initialized to 0), so that an initial velocity observation value is calculated. Then, the disturbance observation unit 1312 calculates a wind power prediction value from the speed observation value and feeds the wind power prediction value back to the state observation unit 1311. Finally, the state observation unit 1311 substitutes the predicted value of wind power input as wind power into the speed calculation model, and updates and calculates the speed observation value of the unmanned aerial vehicle.
It should be noted that fig. 5 describes in detail the structure of the wind observer provided in the embodiment of the present invention, by taking a functional block diagram as an example. According to the inventive idea disclosed in the specification, the steps to be executed and the functions to be implemented, the skilled person can choose to implement the functions of the wind observer by using software, hardware or a combination of software and hardware according to the requirements of the actual situation (for example, the power consumption of a chip, the limitation of heat generation, the cost of a silicon chip or the volume of a chip, etc.). For example, using more software components may reduce the cost and occupied circuit area of the chip and facilitate modification. And the reliability and the operation speed can be improved by using more hardware circuits for implementation.
On the basis of the structural framework of the wind observer shown in fig. 5, the embodiment of the invention also provides a wind speed measuring and calculating method. Fig. 6 is a flowchart of a method of measuring and calculating wind speed according to an embodiment of the present invention.
As shown in fig. 6, the method includes the steps of:
601. and acquiring the flight data information of the unmanned aerial vehicle.
Wherein the flight data information may include a current attitude angle, a speed, and an acceleration of the drone. These flight data information can be obtained through unmanned aerial vehicle's sensor acquisition, belong to the data that are commonly used in unmanned aerial vehicle flight control, can not increase extra cost for unmanned aerial vehicle.
It should be noted that the flight data information to be acquired depends on variables that need to be input when calculating the theoretical flying speed of the drone. Those skilled in the art can adjust or change the data information according to the actual needs, preference setting or precision requirements, etc.
602. And establishing a speed observation model of the unmanned aerial vehicle by using the flight data information so as to obtain a speed observation value of the unmanned aerial vehicle.
The speed observation model is a multi-input single-output function, and can predict the theoretical flight speed (namely, a speed observation value) which the unmanned aerial vehicle should have under the condition of multiple input data information based on the input data information.
In particular, the data information may be flight data from sensors (for example, attitude angle, flight speed and acceleration of the drone), and may also include attribute data (for example, mass of the drone, drag coefficient of the drone while flying, and a non-linear function for calculating the frontal area) pre-stored in a memory, as determined by the characteristics of the drone itself.
In some embodiments, step 602 may be specifically implemented as follows:
first, a velocity equation of the drone is established. Then, according to the speed equation of the unmanned aerial vehicle, the speed observation model of the unmanned aerial vehicle is established so as to obtain the speed observation value of the unmanned aerial vehicle.
The velocity equation of the drone refers to a calculation equation between the velocity of the drone and the flight data information of the drone. Specifically, the velocity equation of the drone may be obtained by a technician according to actual conditions or a summary of a large amount of experimental data, for example, the equation (1) disclosed in the above embodiment may be used.
603. And acquiring an observed value of wind power borne by the unmanned aerial vehicle according to the speed observed value.
According to the principle of the disturbance observer provided by the above embodiment (all disturbances are equivalent to wind disturbance), after the speed observation value is obtained by derivation, the corresponding wind observation value (i.e. the disturbance condition of the wind power currently suffered by the unmanned aerial vehicle) can be determined by calculating the difference between the speed observation value and the actual speed in a suitable conversion manner.
Because the flight speed of unmanned aerial vehicle receives the influence mainly lie in the wind-force interference when flying, the influence that other interference caused is all less. Therefore, the wind observation value calculated and determined on the premise of equivalent setting can be basically regarded as more accurate wind estimation, and the use requirement of wind speed early warning can be met.
Specifically, given a known velocity observation, the corresponding wind observation can be calculated using equation (12):
Figure BDA0002105020900000141
wherein,
Figure BDA0002105020900000142
for the observed value of the wind power change rate of the unmanned aerial vehicle in the y direction,
Figure BDA0002105020900000143
is the observed value of the wind power change rate of the unmanned plane in the x direction,
Figure BDA0002105020900000144
and
Figure BDA0002105020900000145
velocity observations in the x-and y-directions, L, respectively 1x And L 1y To adjust the constants. After the wind power change rate observed value is integrated, the corresponding wind power observed value can be obtained through calculation
Figure BDA0002105020900000146
And
Figure BDA0002105020900000147
to summarize, in a complete anemometry process, the following steps can be roughly included: when the speed observed value is calculated for the first time, unknown variables such as the wind power observed value and the like are initialized to zero, and an initial value is obtained. An initial wind observation is then calculated. And finally, feeding back the wind power observed value to the preset speed calculation model so as to update the wind power observed value and the speed observed value in sequence.
604. And calculating the wind speed in the current flight environment according to the observed value of the wind power.
As will be understood by those skilled in the art, in the case of known wind power, the wind power can be further converted into wind speed and wind direction for subsequent application by a corresponding conversion formula.
In some embodiments, based on the obtained wind observations, the wind observations may also be converted to wind speeds or directions by:
the conversion relation between wind power and wind speed is shown as equation (5):
Figure BDA0002105020900000148
wherein S is the windward area, rho is the air density, and V w Is the wind speed, C d Is a coefficient of resistance, F w Is the wind observation.
In order to simplify the calculation and maintain the consistency with the wind calculation method in the actual conversion, the wind speed can be divided into components in the x direction and the y direction to be calculated respectively (the windward areas in the x direction and the y direction are obtained by approximate calculation of an attitude angle and a nonlinear function, the air density is approximately calculated according to the flying altitude, and the resistance coefficients in the x direction and the y direction are determined by experiments).
Based on the conversion relationship between wind power and wind speed provided by equation (5), the wind observation can be converted to wind speed by equations (6) and (7) as follows:
Figure BDA0002105020900000151
Figure BDA0002105020900000152
wherein, F wy For the wind observation of the unmanned plane in the y direction, F wx For unmanned aerial vehicle wind-force in the x directionObserving the value; rho is the air density of the altitude where the unmanned aerial vehicle is located; s fb Is the windward area S of the unmanned plane flying along the x direction rl Frontal area for unmanned aerial vehicle flying in y direction, C dx Coefficient of drag for unmanned aerial vehicle flying in the x direction, C dy The drag coefficient of the unmanned aerial vehicle flying along the y direction.
In other embodiments, the wind direction in the current environment may also be obtained according to the wind speed in the current environment. The specific conversion manner between the wind speed and the wind direction can be calculated in any suitable manner, and depends on practical application scenarios such as solving the obtained representation form of the wind speed. Specifically, on the premise that the wind speeds in the x direction and the y direction are known, the current wind direction can be calculated by the following equation (15):
β＝ψ+arctan2(-V wx ,-V wy ) (15)
wherein psi is the yaw angle of the unmanned aerial vehicle, and beta is the wind direction.
The wind speed measuring and calculating method provided by the embodiment of the invention is realized by utilizing a disturbance observer. The method does not need to utilize an extra wind speed or a wind sensor, does not depend on a huge database, has the advantages of low implementation cost and good real-time performance, and can be widely applied to an unmanned aerial vehicle system.
Preferably, the wind speed and the wind direction in the current environment obtained by the conversion of the wind speed measurement and calculation method can also be provided to the remote control device 20 and displayed on the remote control device 20, so as to feed back the current wind interference condition of the unmanned aerial vehicle to the user.
In addition to directly sending the wind speed and the wind direction in the current environment to the user terminal and displaying the wind speed and the wind direction on the user terminal, in other embodiments, when the unmanned aerial vehicle flies, the wind speed measuring and calculating method provided by the embodiment of the invention can be periodically operated to obtain the current wind speed and/or the current wind direction to realize the early warning of the unmanned aerial vehicle. Fig. 7 is a flowchart of a method of an unmanned aerial vehicle early warning method according to an embodiment of the present invention.
As shown in fig. 7, the method includes:
701. and acquiring the wind speed in the current environment.
Step 701 may be periodically executed according to a set sampling period, and the wind speed in the current environment is continuously updated, so as to ensure that an early warning is timely given. The sampling period is an empirical value that can be adjusted or set according to the actual situation, for example, a period of 1min or longer.
702. And judging whether the wind speed exceeds a preset value. If yes, go to step 703, if no, go back to step 702, update the current wind speed.
The preset value is a judgment standard preset according to experience or actual conditions of the unmanned aerial vehicle (such as bearing capacity of the unmanned aerial vehicle on wind speed). The unmanned aerial vehicle fault detection method can be a static threshold value preset according to experience, and can also be a dynamic threshold value calculated according to a proper algorithm, and the dynamic threshold value is used for measuring the probability of the unmanned aerial vehicle to be abnormal.
703. A warning signal is emitted.
The warning signal may specifically be represented by any suitable form or type of identifier, for example, a warning flag bit simply represented by 1 and 0 indicates that a warning signal is sent when the value is 1, and indicates that no warning signal is present when the value is 0.
Specifically, the logic for monitor 132 to trigger the warning signal can be expressed by the following equation (8):
Figure BDA0002105020900000161
wherein, V wx Is the wind speed in the x direction, V wy And the wind speed in the y direction is represented by flag, the flag is a warning signal zone bit, a warning signal is sent out when the value is 1, and no warning signal is sent out when the value is 0.
That is, when the sum of the squares of the wind speeds in the x-direction and the y-direction is greater than or equal to the square of the preset alarm threshold value, the monitor 132 will determine that the wind speed satisfies the preset alarm condition and issue an alarm signal.
Of course, the logic decision of triggering the alarm signal shown in equation (8) is merely for illustration and is not used to limit the operation steps of the monitor 132. The technical personnel in the field can adopt other different warning conditions to measure whether the unmanned aerial vehicle has the situation of too large wind and uncontrollable according to the needs of actual conditions.
After receiving the warning signal, the remote control device 20 may feed back corresponding warning prompt information to the user through the display screen or other interactive devices, so as to remind the user to stop the flight of the unmanned aerial vehicle in time, and land to a safe and controllable position.
The specific warning prompt information can be set according to actual conditions, and includes but is not limited to the form of characters or pictures. For example, a word such as "the current wind speed is too high" may be highlighted on the display interface of the remote controller, or the current wind speed is displayed in an icon of a specific color. Furthermore, voice prompts can be broadcasted through a loudspeaker.
The unmanned aerial vehicle early warning method provided by the embodiment of the invention can estimate the wind speed of the flight environment in real time without any wind speed sensor and database, and realizes low cost of wind speed estimation and warning of the unmanned aerial vehicle.
The problem that a user/operator cannot operate in time and explode due to overlarge wind speed can be effectively solved by applying the unmanned aerial vehicle to the unmanned aerial vehicle, and the user is prompted to fly cautiously or select a safe place to land when the wind speed is overlarge.
The following describes in detail a specific process of measuring and calculating the wind speed and warning the wind speed of the unmanned aerial vehicle without depending on a wind speed sensor and a database, with reference to a specific example.
In this embodiment, the drone has one or more basic sensors, and at least the flight data such as the attitude angle (including the pitch angle, the roll angle, and the heading angle), the acceleration, and the flying speed of the drone can be collected in real time.
The x direction and the y direction are two vectors which are perpendicular to each other in the plane where the unmanned aerial vehicle is located. As shown in fig. 1, the x direction is a moving direction in which the drone moves forward and backward, and the y direction is a moving direction in which the drone flies leftward and rightward.
1) Before the unmanned aerial vehicle leaves the factory, the unmanned aerial vehicle is measuredThe quality of human-machine and the windward area under different attitude angles, and based on the windward area data under a plurality of groups of different attitude angles, fitting and determining the nonlinear function f of the windward area and the attitude angle in the x direction and the y direction fb (theta, phi) and f rl (θ,φ)。
2) Acquiring flight data of the unmanned aerial vehicle in the flying process in an indoor windless environment, and calculating and determining the drag coefficient of the unmanned aerial vehicle in the x direction and the y direction through the following formula (9):
Figure BDA0002105020900000171
wherein, a x Acceleration of the unmanned aerial vehicle in the x direction; a is a y For unmanned aerial vehicle acceleration in the y direction, T is the screw pulling force.
The propeller tension T can be determined by estimation from the acceleration and attitude angle by the following equation (10):
Figure BDA0002105020900000172
wherein, a z The acceleration of the unmanned aerial vehicle in the z direction perpendicular to the plane formed by the x direction and the y direction, g is the gravity acceleration, and the acceleration and the g are both positive accelerations in the direction pointing to the ground.
3) And measuring the mass of the determined unmanned aerial vehicle in the steps, taking the nonlinear function of the windward area and the attitude angle in the x direction and the y direction and the resistance coefficient in the x direction and the y direction as attribute data, and recording and storing the attribute data into a memory of the unmanned aerial vehicle.
4) And the unmanned aerial vehicle which completes the attribute data setting periodically collects sensor data in the flying process, and determines the current flight data of the unmanned aerial vehicle through a data fusion algorithm.
5) Based on the flight data from the sensors and the attribute data from the memory, a velocity observation is calculated by the following equation (11):
Figure BDA0002105020900000181
wherein,
Figure BDA0002105020900000182
and
Figure BDA0002105020900000183
the speed change rate observation values of the unmanned aerial vehicle in the x direction and the y direction are respectively output by a speed calculation model;
Figure BDA0002105020900000184
and
Figure BDA0002105020900000185
the speed observed values of the unmanned aerial vehicle in the x direction and the y direction can be obtained after the speed change rate observed values are integrated.
V x Is the actual velocity value in the x direction; v y Is the actual speed value in the y direction; t is the propeller tension; theta is a pitch angle; phi is a rolling angle; rho is the air density of the altitude where the unmanned aerial vehicle is located; s. the fb Is the windward area S of the unmanned plane flying along the x direction rl Frontal area for unmanned aerial vehicle flying in y direction, C dx Coefficient of drag for unmanned aerial vehicle flying in the x direction, C dy Is the drag coefficient when the unmanned plane flies along the y direction,
Figure BDA0002105020900000186
for the wind force of the unmanned aerial vehicle in the x direction,
Figure BDA0002105020900000187
the wind power of the unmanned aerial vehicle in the y direction; m is the mass of the unmanned aerial vehicle; l is a radical of an alcohol 1x And L 1y To adjust the constants.
It should be noted that, during the first calculation, the unknown observed value is included in equation (11)
Figure BDA0002105020900000188
Therefore, in the initial calculation, the output speed observation can be calculated by simply initializing these observations to 0.
6) Based on the velocity observations output by the above steps, a wind observation that causes a difference in the velocity observation and the actual airspeed may be calculated using equation (12) as follows:
Figure BDA0002105020900000189
wherein,
Figure BDA00021050209000001810
for the observed value of the wind power change rate of the unmanned aerial vehicle in the y direction,
Figure BDA00021050209000001811
for the observed value of the wind power change rate of the unmanned aerial vehicle in the x direction,
Figure BDA00021050209000001812
and
Figure BDA00021050209000001813
velocity observations in the x-and y-directions, L, respectively 1x And L 1y To adjust the constants. After the wind power change rate observed value is integrated, the corresponding wind power observed value can be obtained through calculation
Figure BDA00021050209000001814
And
Figure BDA00021050209000001815
as described above, the output wind observation value needs to be fed back to equation (11) in addition to the wind speed warning, and equations (11) and (12) are performed again to update the speed observation value and the wind observation value.
7) Based on the obtained wind observations, it can be converted to wind speed by the following equations (13) and (14):
Figure BDA00021050209000001816
Figure BDA0002105020900000191
wherein, F wx For the wind power of the unmanned aerial vehicle in the y direction, F wx The wind power of the unmanned aerial vehicle in the x direction is adopted; rho is the air density of the altitude where the unmanned aerial vehicle is located; s fb Is the windward area S of the unmanned plane flying along the x direction rl Frontal area for unmanned aerial vehicle flying in y direction, C dx Coefficient of drag, C, for unmanned aerial vehicle flying in the x-direction dy The drag coefficient of the unmanned aerial vehicle flying along the y direction.
FIG. 8 is a graph of wind speed versus time provided by an embodiment of the present invention. As shown in fig. 8, the wind observation value due to the output is composed of a wind observation value in the x direction and a wind observation value in the y direction. Therefore, after the wind speed components in the x direction and the y direction can be respectively calculated corresponding to the wind power observed value, the corresponding two wind speed components are synthesized into the current wind speed borne by the unmanned aerial vehicle, and a curve of the change of the wind speed along with the time is obtained.
8) Obtaining the wind speed based on the conversion, and further calculating the current wind direction according to the posture of the unmanned aerial vehicle by the following equation (15):
β＝ψ+arctan2(-V wx ,-V wy ) (15)
wherein psi is the yaw angle of unmanned aerial vehicle, and beta is the wind direction.
FIG. 9 is a graph of wind direction versus time provided by an embodiment of the present invention. Fig. 9 is a diagram showing a wind direction curve obtained by conversion based on the wind speed curve shown in fig. 8. The computationally determined wind direction angle may also be transmitted to the remote control device 20 for presentation to the user via an interactive device (e.g., a display screen) of the remote control device 20.
9) After the wind speed is updated every time, whether the current wind speed meets the preset warning condition is judged through the formula (16).
Figure BDA0002105020900000192
Wherein, V thr And flag is an alarm signal flag bit. When the value of the flag bit of the warning signal is 1, the preset warning condition is met. And when the flag bit value of the warning signal is 0, the warning signal indicates that the preset warning condition is not met.
Referring to fig. 8, the alarm threshold is a predetermined empirical value. When the wind speed is higher than the alarm threshold value, an alarm signal is sent out to indicate that the wind power is larger at the moment, and a user or an operator needs to be reminded.
10 Remote control device 20 determines whether to issue an alert to the user based on the value of the alert flag. When the remote control device 20 is a remote controller, a prompt character with a large wind speed can be directly displayed on the driving simulation interface to prompt a user to operate in time.
In summary, the wind power measurement and calculation method and the unmanned aerial vehicle early warning method implemented on the basis of the wind power measurement and calculation method provided by the embodiment of the invention do not need to use a sensor related to wind speed and create a huge database, and can implement measurement and calculation of wind power in an algorithm mode on the basis of the existing information, so as to determine the corresponding wind speed and wind direction.
Since it does not rely on wind speed related sensors and databases. Therefore, the hardware implementation cost of the unmanned aerial vehicle is effectively reduced, the defects of large database operation amount, large memory requirement and large time delay are avoided, and the unmanned aerial vehicle has a good application prospect.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.

Claims (11)

1. A wind speed estimation method, comprising:
acquiring flight data information of an unmanned aerial vehicle, wherein the flight data information comprises a current attitude angle, a current speed and a current acceleration of the unmanned aerial vehicle;
establishing a speed equation of the unmanned aerial vehicle;
establishing a speed observation model of the unmanned aerial vehicle according to a speed equation of the unmanned aerial vehicle; variables in the velocity observation model include: wind force experienced by the drone;
inputting the flight data information and the attribute data of the unmanned aerial vehicle into a speed observation model of the unmanned aerial vehicle to obtain a speed observation value of the unmanned aerial vehicle;
wherein, at a time of primary computation, model variables of wind power experienced by the drone are initialized to zero;
acquiring an observed value of wind power borne by the unmanned aerial vehicle according to the difference between the speed observed value and the actual speed of the unmanned aerial vehicle;
in each initialized calculation process, updating a model variable of wind power borne by the unmanned aerial vehicle into an observed value of the wind power borne by the unmanned aerial vehicle obtained in the last calculation;
calculating the wind speed in the current flight environment according to the observed value of the wind power;
wherein, unmanned aerial vehicle's velocity equation is:
Figure 922577DEST_PATH_IMAGE001
Figure 100749DEST_PATH_IMAGE002
wherein,
Figure 582546DEST_PATH_IMAGE003
and
Figure 996210DEST_PATH_IMAGE004
the speeds of the unmanned aerial vehicle in the x direction and the y direction in the flight plane are respectively, T is the tension of a propeller,
Figure 253753DEST_PATH_IMAGE005
is the pitch angle of the drone,
Figure 461881DEST_PATH_IMAGE006
is the roll angle of the drone,
Figure 306340DEST_PATH_IMAGE007
is the air density at the unmanned aerial vehicle flight altitude,
Figure 992536DEST_PATH_IMAGE008
is the windward area of the unmanned aerial vehicle flying along the x direction,
Figure 730685DEST_PATH_IMAGE009
is the windward area of the unmanned aerial vehicle flying along the y direction,
Figure 985080DEST_PATH_IMAGE010
and
Figure 441469DEST_PATH_IMAGE011
for the wind forces respectively experienced by the drone in the x-direction and the y-direction,
Figure 72302DEST_PATH_IMAGE012
and
Figure 664957DEST_PATH_IMAGE013
respectively the drag coefficients of the unmanned aerial vehicle flying along the x direction and flying along the y direction, wherein m is the mass of the unmanned aerial vehicle;
wherein, unmanned aerial vehicle's speed observation model is:
Figure 480466DEST_PATH_IMAGE014
wherein,
Figure 798053DEST_PATH_IMAGE015
updating a velocity change rate observation for the drone in an x direction,
Figure 826052DEST_PATH_IMAGE016
updating a speed change rate observed value for the UAV in a y direction;
Figure 414159DEST_PATH_IMAGE017
the observed value of the speed of the unmanned aerial vehicle in the x direction is obtained;
Figure 400570DEST_PATH_IMAGE018
the observed value of the speed of the unmanned aerial vehicle in the y direction is obtained;
Figure 706917DEST_PATH_IMAGE019
the actual speed value of the unmanned aerial vehicle in the x direction is obtained;
Figure 538607DEST_PATH_IMAGE020
the actual speed value of the unmanned aerial vehicle in the y direction is obtained;
Figure 105855DEST_PATH_IMAGE021
is the propeller tension;
Figure 872954DEST_PATH_IMAGE022
is the pitch angle of the drone;
Figure 791231DEST_PATH_IMAGE023
is the roll angle of the drone;
Figure 818091DEST_PATH_IMAGE024
the air density of the altitude where the unmanned aerial vehicle is located;
Figure 239845DEST_PATH_IMAGE025
is the windward area of the unmanned plane flying along the x direction,
Figure 568058DEST_PATH_IMAGE026
is the windward area of the unmanned aerial vehicle flying along the y direction,
Figure 317840DEST_PATH_IMAGE027
is the drag coefficient of the unmanned plane flying along the x direction,
Figure 22491DEST_PATH_IMAGE028
is the drag coefficient when the unmanned plane flies along the y direction,
Figure 298751DEST_PATH_IMAGE029
for the observed value of the wind power of the unmanned plane in the x direction,
Figure 673232DEST_PATH_IMAGE030
the wind power observed value of the unmanned aerial vehicle in the y direction is obtained;
Figure 441468DEST_PATH_IMAGE031
the mass of the drone;
Figure 153072DEST_PATH_IMAGE032
and
Figure 814997DEST_PATH_IMAGE033
is a speed adjustment coefficient for correcting errors.
2. The method of claim 1, wherein the frontal area is determined by calculating:
Figure 593335DEST_PATH_IMAGE034
wherein,
Figure 707922DEST_PATH_IMAGE035
is the windward area of the unmanned plane flying along the x direction,
Figure 364162DEST_PATH_IMAGE036
the frontal area of the unmanned aerial vehicle flying along the x direction;
Figure 880594DEST_PATH_IMAGE037
when the attitude angle is 0, the windward area of the unmanned aerial vehicle flying along the x direction;
Figure 862456DEST_PATH_IMAGE038
when the attitude angle is 0, the windward area of the unmanned aerial vehicle flying along the y direction;
Figure 464339DEST_PATH_IMAGE039
and
Figure 924270DEST_PATH_IMAGE040
is a non-linear function;
Figure 934689DEST_PATH_IMAGE041
is a pitch angle;
Figure 680929DEST_PATH_IMAGE042
is the roll angle.
3. The method of claim 1, wherein the propeller tension is calculated by the following equation:
Figure 504528DEST_PATH_IMAGE043
wherein,
Figure 768150DEST_PATH_IMAGE044
is the acceleration of the drone in the z direction,
Figure 728016DEST_PATH_IMAGE045
is the acceleration of gravity; the z direction is perpendicular to a plane formed by the x direction and the y direction;
Figure 645157DEST_PATH_IMAGE041
is a pitch angle;
Figure 96998DEST_PATH_IMAGE042
is the roll angle; and m is the mass of the unmanned aerial vehicle.
4. The method of claim 3, wherein the drag coefficient is calculated by the following equation:
Figure 23365DEST_PATH_IMAGE046
wherein,
Figure 978683DEST_PATH_IMAGE047
is the drag coefficient of the unmanned plane flying along the x direction,
Figure 332304DEST_PATH_IMAGE048
is the drag coefficient of the unmanned plane flying along the y direction,
Figure 504397DEST_PATH_IMAGE049
under the windless condition, the acceleration of the unmanned aerial vehicle in the x direction;
Figure 703297DEST_PATH_IMAGE050
under the windless condition, the acceleration of the unmanned aerial vehicle in the y direction;
Figure 903334DEST_PATH_IMAGE051
is the propeller tension;
Figure 303223DEST_PATH_IMAGE052
is a pitch angle;
Figure 323131DEST_PATH_IMAGE053
is the roll angle;
Figure 732247DEST_PATH_IMAGE054
the air density of the altitude where the unmanned aerial vehicle is located;
Figure 786791DEST_PATH_IMAGE055
is the windward area of the unmanned aerial vehicle flying along the x direction,
Figure 357581DEST_PATH_IMAGE056
is the windward area of the unmanned plane flying along the y direction,
Figure 864785DEST_PATH_IMAGE031
is the quality of the unmanned aerial vehicle.
5. The method of claim 2, wherein obtaining the observation of the wind force experienced by the drone based on the velocity observation comprises:
calculating an observed value of wind power borne by the unmanned aerial vehicle according to the following formula:
Figure 936646DEST_PATH_IMAGE057
wherein,
Figure 225457DEST_PATH_IMAGE058
for the observed value of the wind power change rate of the unmanned aerial vehicle in the y direction,
Figure 91782DEST_PATH_IMAGE059
is the observed value of the wind power change rate of the unmanned plane in the x direction,
Figure 961649DEST_PATH_IMAGE060
is composed of
Figure 837201DEST_PATH_IMAGE061
Represents the wind observation in the x-direction,
Figure 600758DEST_PATH_IMAGE062
is composed of
Figure 513350DEST_PATH_IMAGE063
Represents the wind observation in the y-direction,
Figure 995147DEST_PATH_IMAGE031
is the mass of the unmanned aerial vehicle,
Figure 284177DEST_PATH_IMAGE064
and
Figure 167820DEST_PATH_IMAGE065
the wind power adjustment coefficient;
Figure 110368DEST_PATH_IMAGE066
the speed observed value of the unmanned aerial vehicle in the x direction is obtained;
Figure 453362DEST_PATH_IMAGE067
and the speed observed value of the unmanned aerial vehicle in the y direction is obtained.
6. The method of claim 5, wherein said calculating a wind speed for a current environment from said observed value of said wind power comprises:
calculating the wind speed of the unmanned aerial vehicle in the current environment by using the following formula:
Figure 405138DEST_PATH_IMAGE068
wherein,
Figure 18653DEST_PATH_IMAGE069
is the wind power observed value of the unmanned aerial vehicle in the x direction,
Figure 663261DEST_PATH_IMAGE070
the wind power observed value of the unmanned aerial vehicle in the y direction is obtained;
Figure 854071DEST_PATH_IMAGE071
the air density of the altitude where the unmanned aerial vehicle is located;
Figure 484903DEST_PATH_IMAGE072
is the windward area of the unmanned plane flying along the x direction,
Figure 77559DEST_PATH_IMAGE073
is the windward area of the unmanned aerial vehicle flying along the y direction,
Figure 768434DEST_PATH_IMAGE074
is the drag coefficient of the unmanned plane flying along the x direction,
Figure 180961DEST_PATH_IMAGE075
the drag coefficient when the unmanned aerial vehicle flies along the y direction.
7. The method of claim 1, further comprising:
and acquiring the wind direction under the current flight environment according to the wind speed under the current flight environment.
8. The method of claim 7, wherein the wind direction in the current environment satisfies the following expression:
Figure 740118DEST_PATH_IMAGE076
wherein,
Figure 826761DEST_PATH_IMAGE077
is the yaw angle of the unmanned aerial vehicle,
Figure 547592DEST_PATH_IMAGE078
is the direction of the wind,
Figure 853940DEST_PATH_IMAGE079
for the wind speed in the x-direction,
Figure 685629DEST_PATH_IMAGE080
is the wind speed in the y direction.
9. The method of claim 1, further comprising:
and when the wind speed in the current flight environment exceeds a preset value, sending an alarm signal to a user.
10. The method according to claim 7 or 8, characterized in that the method further comprises:
and sending the wind speed and the wind direction under the current flight environment to a user terminal and displaying the wind speed and the wind direction on the user terminal.
11. An unmanned aerial vehicle, comprising:
a body;
the machine arm is connected with the machine body;
the power device is arranged on the horn and used for providing flying power for the unmanned aerial vehicle; and
the flight controller is arranged on the airplane body;
wherein the flight controller includes:
at least one processor; and
a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the wind velocity estimation method of any of claims 1-9.
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